Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

Bioanalytical applications of microfluidic devices

The first part of the thesis describes a new patterning technique--microfluidic contact printing--that combines several of the desirable aspects of microcontact printing and microfluidic patterning and addresses some of their important limitations through the integration of a track-etched polycarbonate (PCTE) membrane. Using this technique, biomolecules (e.g., peptides, polysaccharides, and proteins) were printed in high fidelity on a receptor modified polyacrylamide hydrogel substrate. The patterns obtained can be controlled through modifications of channel design and secondary programming via selective membrane wetting. The protocols support the printing of multiple reagents without registration steps and fast recycle times. The second part describes a non-enzymatic, isothermal method to discriminate single nucleotide polymorphisms (SNPs). SNP discrimination using alkaline dehybridization has long been neglected because the pH range in which thermodynamic discrimination can be done is quite narrow. We found, however, that SNPs can be discriminated by the kinetic differences exhibited in the dehybridization of PM and MM DNA duplexes in an alkaline solution using fluorescence microscopy. We combined this method with multifunctional encoded hydrogel particle array (fabricated by stop-flow lithography) to achieve fast kinetics and high versatility. This approach may serve as an effective alternative to temperature-based method for analyzing unamplified genomic DNA in point-of-care diagnostic.

Heat dissipation from carbon nano-electronics

The incorporation of graphitic compounds such as carbon nanotubes (CNTs) and graphene into nano-electronic device packaging holds much promise for waste heat management given their high thermal conductivities. However, as these graphitic materials must be used in together with other semiconductor/insulator materials, it is not known how thermal transport is affected by the interaction. Using different simulation techniques, in this thesis, we evaluate the thermal transport properties - thermal boundary conductance (TBC) and thermal conductivity - of CNTs and single-layer graphene in contact with an amorphous SiO2 (a-SiO2) substrate. First, the theoretical methodologies and concepts used in our simulations are presented. In particular, two concepts are described in detail as they are necessary for the understanding of the subsequent chapters. The first is the linear response Green-Kubo (GK) theory of thermal boundary conductance (TBC), which we develop in this thesis, and the second is the spectral energy density method, which we use to directly compute the phonon lifetimes and thermal transport coefficients. After we set the conceptual foundations, the TBC of the CNT-SiO2 interface is computed using non- equilibrium molecular dynamics (MD) simulations and the new Green-Kubo method that we have developed. Its dependence on temperature, the strength of the interaction with the substrate, and tube diameter are evaluated. To gain further insight into the phonon dynamics in supported CNTs, the scattering rates are computed using the spectral energy density (SED) method. With this method, we are able to distinguish the different scattering mechanisms (boundary and CNT-substrate phonon-phonon) and rates. The phonon lifetimes in supported CNTs are found to be reduced by contact with the substrate and we use that lifetime reduction to determine the change in CNT thermal conductivity. Next, we examine thermal transport in graphene supported on SiO2. The phonon contribution to the TBC of the graphene-SiO2 interface is computed from MD simulations and found to agree well with experimentally measured values. We derive the theory of remote phonon scattering of graphene electrons and compute the heat transfer coefficient dependence on doping level and temperature. The thermal boundary conductance from remote phonon scattering is found to be an order of magnitude smaller than that of the phonon contribution. The in-plane thermal conductivity of supported graphene is calculated from MD simulations. The experimentally measured order of magnitude reduction in thermal conductivity is reproduced in our simulations. We show that this reduction is due to the damping of the flexural (ZA) modes. By varying the interaction between graphene and the substrate, the ZA modes hybridize with the substrate Rayleigh modes and the dispersion of the hybridized modes is found to linearize in the strong coupling limit, leading to an increased thermal conductance in the composite structure.